Search Results - Graph algorithms and theory

A graph‐theory algorithm for rapid protein side‐chain prediction by Canutescu, Adrian A., Shelenkov, Andrew A., Dunbrack, Roland L.

“… A new algorithm for SCWRL is presented that uses results from graph theory to solve the combinatorial problem encountered in the side…”
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Multi-response optimization in WEDM process of Al–Si alloy using TLBO-graph theory algorithm towards sustainability by Venkatarao, Kaki, Reddy, Malla Chaitanya, Kumar, Yekula Prasanna, Raju, Lam Suvarna, Rao, Bonula Rama, Azad, Duppala

“… The HTLBO comprises teaching and learning–based optimization technique and graph theory algorithm to improve WEDM performance…”
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Graph Theory Algorithms of Hamiltonian Cycle from Quasi-Spanning Tree and Domination Based on Vizing Conjecture by Anuradha, T., Lakshmi Surekha, T., Nuthakki, Praveena, Domathoti, Bullarao, Ghorai, Ganesh, Shami, Faria Ahmed

“…In this study, from a tree with a quasi-spanning face, the algorithm will route Hamiltonian cycles…”
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Skywalker: Efficient Alias-Method-Based Graph Sampling and Random Walk on GPUs by Wang, Pengyu, Li, Chao, Wang, Jing, Wang, Taolei, Zhang, Lu, Leng, Jingwen, Chen, Quan, Guo, Minyi

“…Graph sampling and random walk operations, capturing the structural properties of graphs, are playing an important role today as we cannot directly adopt computing-intensive algorithms on large-scale graphs…”
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Application of Graph Theory Algorithms in Non-disjoint Functional Decomposition of Specific Boolean Functions by Mazurkiewicz, Tomasz

“… A heuristic method for finding non-disjoint decomposition has been proposed lately. In this paper, we examine how the usage of different graph theory techniques affects the computation time and the quality of the solution obtained…”
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BlasPart: A Deterministic Parallel Partitioner for Balanced Large-Scale Hypergraph Partitioning by Tong, Shengbo, Pei, Chunyan, Yu, Wenjian

“… Thus, fast and high-quality deterministic partitioning algorithms are largely in demand…”
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Graph Algorithms and Graph Theory-Symmetry Special Issue by Lafond, Manuel

“…Understanding the structure of graphs and the interplay between their parameters is a fundamental question that has been actively researched over the last…”
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DS-GL: Advancing Graph Learning via Harnessing Nature's Power within Scalable Dynamical Systems by Song, Ruibing, Wu, Chunshu, Liu, Chuan, Li, Ang, Huang, Michael, Geng, Tony Tong

“…With the rapid digitization of the world, an increasing number of real-world applications are turning to non-Euclidean data, modeled as graphs…”
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PIMGCN: A ReRAM-Based PIM Design for Graph Convolutional Network Acceleration by Yang, Tao, Li, Dongyue, Han, Yibo, Zhao, Yilong, Liu, Fangxin, Liang, Xiaoyao, He, Zhezhi, Jiang, Li

“…Graph Convolutional Network (GCN) is a promising but computing- and memory-intensive learning model…”
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Adaptive multi-objective distribution network reconfiguration using multi-objective discrete particles swarm optimisation algorithm and graph theory by Andervazh, Mohammad-Reza, Olamaei, Javad, Haghifam, Mahmoud-Reza

“… Probabilistic heuristics and graph theory techniques are employed to improve the stochastic random search of the algorithm self-adaptively during the optimisation process…”
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iG-kway: Incremental k-way Graph Partitioning on GPU by Lee, Wan Luan, Jiang, Shui, Lin, Dian-Lun, Chang, Che, Zhang, Boyang, Chung, Yi-Hua, Schlichtmann, Ulf, Ho, Tsung-Yi, Huang, Tsung-Wei

“…Recent advances in GPU-accelerated graph partitioning have achieved significant performance gains but remain limited to full graph partitioning, lacking support for incremental updates…”
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GPart: A GNN-Enabled Multilevel Graph Partitioner by Chen, Magi, Wang, Ting-Chi

“…This paper introduces GPart, a scalable multilevel framework for graph partitioning that integrates GNN embeddings with efficient coarsening and refinement techniques…”
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SumPA: Efficient Pattern-Centric Graph Mining with Pattern Abstraction by Gui, Chuangyi, Liao, Xiaofei, Zheng, Long, Yao, Pengcheng, Wang, Qinggang, Jin, Hai

“…Graph mining aims to explore interesting structural information of a graph. Pattern-centric systems typically transform a generic-purpose graph mining problem into a series of subgraph matching problems for high performance…”
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LearnGraph: A Learning-Based Architecture for Dynamic Graph Processing by Zhang, Lingling, Wu, Yijian, Jiang, Hong, Zhou, Ziyu, Lu, Tiancheng

“…Dynamic graph processing systems using conventional array-based architectures face significant throughput limitations due to inefficient memory access and index management…”
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The Readability of Path-Preserving Clusterings of Graphs by Archambault, Daniel, Purchase, Helen C., Pinaud, Bruno

“…Graph visualization systems often exploit opaque metanodes to reduce visual clutter and improve the readability of large graphs…”
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EMGraph: Fast Learning-Based Electromigration Analysis for Multi-Segment Interconnect Using Graph Convolution Networks by Jin, Wentian, Chen, Liang, Sadiqbatcha, Sheriff, Peng, Shaoyi, Tan, Sheldon X.-D.

“… VLSI multisegment interconnect trees can be naturally viewed as graphs. Based on this observation, we propose a new graph convolution network (GCN…”
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The Logic of Graph Neural Networks by Grohe, Martin

“…Graph neural networks (GNNs) are deep learning architectures for machine learning problems on graphs…”
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SGIRR: Sparse Graph Index Remapping for ReRAM Crossbar Operation Unit and Power Optimization by Wang, Cheng-Yuan, Chang, Yao-Wen, Chang, Yuan-Hao

“… This paper proposes a two-stage algorithm with a crossbar OU-aware scheme for sparse graph index remapping for ReRAM (SGIRR…”
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Dynamic Allocation of Processor Cores to Graph Applications on Commodity Servers by Pons, Lucia, Sahuauillo, Julio, Jones, Timothy M.

“…Graph processing is increasingly adopted to solve problems that span many application domains, including scientific computing, social networks, and big-data analytics…”
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Supporting Supply Chain Risk Management: An Innovative Approach Using Graph Theory and Forecasting Algorithms by Wasik, Edmund, Sidor, Tomasz, Wolowiec, Tomasz, Piwkowski, Jacek, Jasienski, Michal

“…Originality/Value: What is new is the use of graph theory to review supply chain risks and algorithms for applications supporting supply chain management…”
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